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acc: Include full output for default-python/classic (#2391)
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## Tests
Include full output of default-python/classic so it can be used as a
base for diffs in cloud tests #2383
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denik authored Feb 27, 2025
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# Typings for Pylance in Visual Studio Code
# see https://github.com/microsoft/pyright/blob/main/docs/builtins.md
from databricks.sdk.runtime import *
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{
"recommendations": [
"databricks.databricks",
"ms-python.vscode-pylance",
"redhat.vscode-yaml"
]
}
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{
"python.analysis.stubPath": ".vscode",
"jupyter.interactiveWindow.cellMarker.codeRegex": "^# COMMAND ----------|^# Databricks notebook source|^(#\\s*%%|#\\s*\\<codecell\\>|#\\s*In\\[\\d*?\\]|#\\s*In\\[ \\])",
"jupyter.interactiveWindow.cellMarker.default": "# COMMAND ----------",
"python.testing.pytestArgs": [
"."
],
"python.testing.unittestEnabled": false,
"python.testing.pytestEnabled": true,
"python.analysis.extraPaths": ["src"],
"files.exclude": {
"**/*.egg-info": true,
"**/__pycache__": true,
".pytest_cache": true,
},
}
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# my_default_python

The 'my_default_python' project was generated by using the default-python template.

## Getting started

1. Install the Databricks CLI from https://docs.databricks.com/dev-tools/cli/databricks-cli.html

2. Authenticate to your Databricks workspace, if you have not done so already:
```
$ databricks configure
```
3. To deploy a development copy of this project, type:
```
$ databricks bundle deploy --target dev
```
(Note that "dev" is the default target, so the `--target` parameter
is optional here.)
This deploys everything that's defined for this project.
For example, the default template would deploy a job called
`[dev yourname] my_default_python_job` to your workspace.
You can find that job by opening your workpace and clicking on **Workflows**.
4. Similarly, to deploy a production copy, type:
```
$ databricks bundle deploy --target prod
```
Note that the default job from the template has a schedule that runs every day
(defined in resources/my_default_python.job.yml). The schedule
is paused when deploying in development mode (see
https://docs.databricks.com/dev-tools/bundles/deployment-modes.html).
5. To run a job or pipeline, use the "run" command:
```
$ databricks bundle run
```
6. Optionally, install the Databricks extension for Visual Studio code for local development from
https://docs.databricks.com/dev-tools/vscode-ext.html. It can configure your
virtual environment and setup Databricks Connect for running unit tests locally.
When not using these tools, consult your development environment's documentation
and/or the documentation for Databricks Connect for manually setting up your environment
(https://docs.databricks.com/en/dev-tools/databricks-connect/python/index.html).
7. For documentation on the Databricks asset bundles format used
for this project, and for CI/CD configuration, see
https://docs.databricks.com/dev-tools/bundles/index.html.
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# This is a Databricks asset bundle definition for my_default_python.
# See https://docs.databricks.com/dev-tools/bundles/index.html for documentation.
bundle:
name: my_default_python
uuid: [UUID]

include:
- resources/*.yml

targets:
dev:
# The default target uses 'mode: development' to create a development copy.
# - Deployed resources get prefixed with '[dev my_user_name]'
# - Any job schedules and triggers are paused by default.
# See also https://docs.databricks.com/dev-tools/bundles/deployment-modes.html.
mode: development
default: true
workspace:
host: [DATABRICKS_URL]

prod:
mode: production
workspace:
host: [DATABRICKS_URL]
# We explicitly deploy to /Workspace/Users/[USERNAME] to make sure we only have a single copy.
root_path: /Workspace/Users/[USERNAME]/.bundle/${bundle.name}/${bundle.target}
permissions:
- user_name: [USERNAME]
level: CAN_MANAGE
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# Fixtures

This folder is reserved for fixtures, such as CSV files.

Below is an example of how to load fixtures as a data frame:

```
import pandas as pd
import os

def get_absolute_path(*relative_parts):
if 'dbutils' in globals():
base_dir = os.path.dirname(dbutils.notebook.entry_point.getDbutils().notebook().getContext().notebookPath().get()) # type: ignore
path = os.path.normpath(os.path.join(base_dir, *relative_parts))
return path if path.startswith("/Workspace") else "/Workspace" + path
else:
return os.path.join(*relative_parts)

csv_file = get_absolute_path("..", "fixtures", "mycsv.csv")
df = pd.read_csv(csv_file)
display(df)
```
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.databricks/
build/
dist/
__pycache__/
*.egg-info
.venv/
scratch/**
!scratch/README.md
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[pytest]
testpaths = tests
pythonpath = src
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## requirements-dev.txt: dependencies for local development.
##
## For defining dependencies used by jobs in Databricks Workflows, see
## https://docs.databricks.com/dev-tools/bundles/library-dependencies.html

## Add code completion support for DLT
databricks-dlt

## pytest is the default package used for testing
pytest

## Dependencies for building wheel files
setuptools
wheel

## databricks-connect can be used to run parts of this project locally.
## See https://docs.databricks.com/dev-tools/databricks-connect.html.
##
## databricks-connect is automatically installed if you're using Databricks
## extension for Visual Studio Code
## (https://docs.databricks.com/dev-tools/vscode-ext/dev-tasks/databricks-connect.html).
##
## To manually install databricks-connect, either follow the instructions
## at https://docs.databricks.com/dev-tools/databricks-connect.html
## to install the package system-wide. Or uncomment the line below to install a
## version of db-connect that corresponds to the Databricks Runtime version used
## for this project.
#
# databricks-connect>=15.4,<15.5
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# The main job for my_default_python.
resources:
jobs:
my_default_python_job:
name: my_default_python_job

trigger:
# Run this job every day, exactly one day from the last run; see https://docs.databricks.com/api/workspace/jobs/create#trigger
periodic:
interval: 1
unit: DAYS

email_notifications:
on_failure:
- [USERNAME]

tasks:
- task_key: notebook_task
job_cluster_key: job_cluster
notebook_task:
notebook_path: ../src/notebook.ipynb

- task_key: refresh_pipeline
depends_on:
- task_key: notebook_task
pipeline_task:
pipeline_id: ${resources.pipelines.my_default_python_pipeline.id}

- task_key: main_task
depends_on:
- task_key: refresh_pipeline
job_cluster_key: job_cluster
python_wheel_task:
package_name: my_default_python
entry_point: main
libraries:
# By default we just include the .whl file generated for the my_default_python package.
# See https://docs.databricks.com/dev-tools/bundles/library-dependencies.html
# for more information on how to add other libraries.
- whl: ../dist/*.whl

job_clusters:
- job_cluster_key: job_cluster
new_cluster:
spark_version: 15.4.x-scala2.12
node_type_id: i3.xlarge
data_security_mode: SINGLE_USER
autoscale:
min_workers: 1
max_workers: 4
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# The main pipeline for my_default_python
resources:
pipelines:
my_default_python_pipeline:
name: my_default_python_pipeline
## Specify the 'catalog' field to configure this pipeline to make use of Unity Catalog:
# catalog: catalog_name
target: my_default_python_${bundle.target}
libraries:
- notebook:
path: ../src/dlt_pipeline.ipynb

configuration:
bundle.sourcePath: ${workspace.file_path}/src
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# scratch

This folder is reserved for personal, exploratory notebooks.
By default these are not committed to Git, as 'scratch' is listed in .gitignore.
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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"application/vnd.databricks.v1+cell": {
"cellMetadata": {
"byteLimit": 2048000,
"rowLimit": 10000
},
"inputWidgets": {},
"nuid": "[UUID]",
"showTitle": false,
"title": ""
}
},
"outputs": [],
"source": [
"import sys\n",
"\n",
"sys.path.append(\"../src\")\n",
"from my_default_python import main\n",
"\n",
"main.get_taxis(spark).show(10)"
]
}
],
"metadata": {
"application/vnd.databricks.v1+notebook": {
"dashboards": [],
"language": "python",
"notebookMetadata": {
"pythonIndentUnit": 2
},
"notebookName": "ipynb-notebook",
"widgets": {}
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.11.4"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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"""
setup.py configuration script describing how to build and package this project.
This file is primarily used by the setuptools library and typically should not
be executed directly. See README.md for how to deploy, test, and run
the my_default_python project.
"""

from setuptools import setup, find_packages

import sys

sys.path.append("./src")

import datetime
import my_default_python

local_version = datetime.datetime.utcnow().strftime("%Y%m%d.%H%M%S")

setup(
name="my_default_python",
# We use timestamp as Local version identifier (https://peps.python.org/pep-0440/#local-version-identifiers.)
# to ensure that changes to wheel package are picked up when used on all-purpose clusters
version=my_default_python.__version__ + "+" + local_version,
url="https://databricks.com",
author="[USERNAME]",
description="wheel file based on my_default_python/src",
packages=find_packages(where="./src"),
package_dir={"": "src"},
entry_points={
"packages": [
"main=my_default_python.main:main",
],
},
install_requires=[
# Dependencies in case the output wheel file is used as a library dependency.
# For defining dependencies, when this package is used in Databricks, see:
# https://docs.databricks.com/dev-tools/bundles/library-dependencies.html
"setuptools"
],
)
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